The problem: a backend that survives the demo, not production
Many systems work perfectly in the demo and start failing once real traffic arrives: slow queries, fragile integrations that break with the first external change, architectures that don't separate what should be separated. The cost of a poorly designed backend doesn't show up at first — it shows up six months later, when changing it is already an expensive migration.
How we solve it
- Architecture designed for real volume, not demo volume — we size for expected growth, not for day one.
- Clear, documented APIs, designed to integrate with what you already have, including AI or computer vision models that need to be served in real time.
- Separation of concerns: what changes fast (business logic) kept separate from what must stay stable (data, authentication) — so one change doesn't break the whole system.
- Monitoring from day one of deployment: we don't wait for something to fail to find out.
Expected result
A well-built backend is measured by what doesn't happen: outages, emergency migrations, growth bottlenecks. The specific success criteria (response times, load capacity) are defined in Phase 2 of our methodology, based on your case.
Technology stack we use
REST / GraphQL APIs
Chosen for what best fits how the data will be consumed, without technology dogma.
Relational & non-relational databases
Chosen by data type, not by default.
AWS / Azure / Google Cloud
Scalable infrastructure on the provider your company already uses.
AI model deployment
We serve PyTorch/TensorFlow models as part of the same backend architecture, not as disconnected parallel systems.
Methodology
Related services
Backend is the foundation supporting mobile apps, machine learning models and any computer vision project in production.